Artificially intelligent order processing system

ABSTRACT

An improved speech-based/natural language point-of-sale customer order system which is useful for any business that interacts with customers through speech or sound. Despite the advances in speech recognition, currently available voice ordering interfaces have proven to be unintuitive and lack reliability. Voice recognition has so far proven to be inefficient in retail contexts, and therefore voice recognition has so far achieved a low level of usage penetration in the retail sector. The present invention facilitates the automated operation of the ordering function of a drive-through restaurant, fast food restaurant or other business establishment by replacing an employee or other means of capturing order data with an ordering system employing a highly accurate speech recognition component that is able to be trained to recognize a wide vocabulary of words, and associate tones and other metadata in a manner not previously achieved in speech-to-text systems.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Application62/633,240, filed Feb. 21, 2018; and claims priority to U.S. applicationSer. No. 15/723,573, filed Oct. 3, 2017, which claims priority to U.S.Provisional Application 62/478,454, filed Mar. 29, 2017, each of whichare hereby incorporated by reference in their entirety.

FIELD OF INVENTION

This invention relates broadly to order processing systems forcommercial transactions. More specifically, this invention relates toorder processing systems to facilitate the ordering or selection ofproducts or services by a customer using natural language.

BACKGROUND OF THE INVENTION

Speech recognition systems are systems that utilize a machine toidentify words or phrases in spoken language and convert them intomachine readable text or instructions. Early speech recognitionapplications included simple tasks such as voice dialing (e.g., “callhome” for a phone), simple data entry (e.g., entering a credit cardnumber or account number audibly), speech-to-text processing (e.g., wordprocessors or emails). As speech-to-text recognition systems have becomemore advanced, the applications amenable to these systems has alsoadvanced. For example, U.S. Pat. No. 9,318,108 B2 to Gruber et al. isdirected to an intelligent automated assistant system that engages withthe user in an integrated, conversational manner using natural languagedialog, and invokes external services when appropriate to obtaininformation or perform various actions. Further systems have beendeveloped whereby a device with speech recognition capabilities cancontrol the functions of a variety of secondary devices around the homeor at a commercial enterprise. For example, U.S. Pat. No. 9,698,999 B2to Mutagi teaches systems and techniques for controlling a secondarydevice by natural language input using a primary speech-responsivedevice. The secondary device can be a device not traditionallyconsidered “smart”, such as a desk lamp, which can be turned on and offby natural language inputs to the primary speech-responsive device.

Many businesses, including banks, retail stores and restaurants rely onverbal orders from customers. Slow, inaccurate or inefficient capture ofverbal orders can frustrate customers and lead to lower sales. This isespecially true in fast food restaurants. Fast food restaurants, orquick serve restaurants, are restaurants that specialize in food thatcan be prepared and served quickly. While many of these types ofrestaurants have placed an increased focus on the quality of the foodserved, a principal focus remains serving the customer quickly andaccurately for the convenience of the customer. The process begins whenthe customer engages the restaurant's order processing system, be thatan employee taking an order or, more recently, interacting with a touchscreen system or other touch-activated physical interface to make orderselections. Streamlining the order processing system can dramaticallyenhance the speed of the over-all process and enhance customersatisfaction, while driving down labor-associated expenditures.

Depending on the level of customer traffic, a delay can often resultwhen the restaurant employees are busy fulfilling other service tasks,such as collecting payment and delivering food. This delay can besignificantly frustrating to customers wishing to place an order. Inaddition, significant amounts of time can be devoted to receiving ordersby restaurant employees, which can limit their productivity in otherareas of their job function. Moreover, the intense time demands onrestaurant workers can lead to less pleasant interactions withcustomers, which can be critically impact the first impression that iscreated with the ordering process. And the time constraints may lead tomissed opportunities for additional sales, such as through therecommendation of complimentary or new products that are available forpurchase.

Attempts have been made to develop speech-based natural languageordering systems. These systems have numerous limitations that havereduced their acceptance by customers. For example, these systems havehad a limited vocabulary and are poor at recognizing words spoken atdifferent speeds or with accents in a manner analogous to that of humancapability. They are also poor at recognizing tone, such as tone thatcould detect growing frustration with the process requiring humanintervention. Moreover, these systems often fail to make keyassociations between products ordered and miss the opportunity to sellor ‘up-sell’ additional items. This can lead to an unwillingness toadopt a system due to concerns over lost revenue opportunities. Thepresent invention overcomes these short-comings as will become apparentin the foregoing description of the artificially-intelligent, naturallanguage order processing system as taught herein.

SUMMARY OF THE INVENTION

The preferred embodiment of the invention consists of an artificiallyintelligent order processing system. Alternative embodiments of theinvention comprise methods of deployment of the artificially intelligentorder processing system. The present inventor has discovered that whenconfigured as described herein, the artificially intelligent orderprocessing system is useful for any business that interacts withcustomers through speech or sound.

In an embodiment, the present invention comprises a method of training anatural language ordering system. The method comprises the steps of (1)providing an audio stream of customer ordering transactions; (2) slicingthe audio stream into short clips (also variously referred to herein as“audio clips”); (3) transcribing the short clips into text using atranscription unit; (4) adding metadata tags to the transcribed textusing the transcription unit; (5) training an artificial intelligencenetwork by populating the network with the transcribed text having themetadata tags. The short clips optionally comprise of sentences,phrases, time-limited clips (e.g. 2 seconds, 3 seconds, 4 seconds, 5seconds, 6 seconds, 8 seconds, 10 seconds, or 15 seconds). In anembodiment, the method further includes the steps of scanning the shortclips for a set of predefined parameters or words and providing theshort clips having predetermined parameters or words to a transcriptionunit for transcribing. By keeping certain clips and discarding otherclips, the present inventor has discovered that when configured asdescribed herein, the artificially intelligent order processing systemavoids overloading the transcription unit. In an embodiment, thetranscription unit consists of a crowd source platform. The presentinventor has discovered that in the context of invention, crowd sourceplatforms allow for incorporation human perception and transcription ofthe short clips, without requiring a dedicated staff to review the shortclips. In an embodiment, the step of transcribing the short clips in thetraining phase is performed by a human. The present inventor hasdiscovered that human transcription at the training stage enhances thefidelity of the recognition in later stages. Metadata tags areoptionally added during transcription in embodiments. The metadata tagsoptionally include data on tone or inflection. The present inventor hasnoted that aspects such as tone or inflection are often difficult toaccurately capture and recognize, especially initially, using previouslyexisting speech-to-text processors. In an alternative embodiment, aspeech-to-text processor as well known to those skilled in the art isutilized to achieve transcription.

In an embodiment, the method further comprises the steps of (1)identifying a word or phrase in the text, such as by using thespeech-to-text processor, and providing meaning for the identified wordor phrase using a natural language processor. In this manner, theartificially intelligent order processing system optionally attributesmeaning from the audio clip to be used to assemble an order. Followingattribution of meaning from the words or phrases by the artificiallyintelligent order processing system, an order can be generated with abusiness processor using the identified word or phrase from the naturallanguage processor. In addition, the natural language processor isconfigured or trained in an embodiment to trigger an alert when awarning condition is encountered. Warning conditions optionally compriseutterances such as a word or phrase indicative of customerdissatisfaction or confusion. In an advantageous embodiment, the audiostream used for text transcription is a stream from a customeraccessible microphone to a base station, which can then be transmittedto one or more headsets. The method in an embodiment further comprisesthe step of decreasing the strength of the audio stream prior totransmission to the transcription unit.

In another embodiment, the invention comprises a method of speechrecognition-based order processing. The method comprises the steps of(1) providing an audio stream of a customer order; (2) providing anorder processor having a speech recognition module trained usingartificial intelligence programs; (3) converting a word or words in theaudio stream to text using the speech recognition module; (4) processingthe text communication with the speech recognition module to identify aword or words in the text of the converted audio stream according to aprevious spoken word training; (5) providing a natural languageprocessor having order assembly capabilities and exception detectioncapabilities, wherein the natural language processor receives recognizedtext from the order processor and creates or modifies an order basedupon the recognized text; (6) generating an order with the naturallanguage processor; (7) alerting an auditor of detected exceptions inthe order; and (8) transmitting the order to a business processor,wherein the business processor communicates with a point-of-sale systemto collect payment and provide notifications to release the orderedproduct or service. In an advantageous embodiment, the order isprocessed in real time. In further advantageous embodiments the businessprocessor will receive order information from the NLP and perform thesteps of associating the order items with one or more additional menuitems or options associated with the order items and querying thecustomer about the one or more additional menu items or optionsassociated with the order items. The NLP can then update the order basedupon the customer response to the query. In this manner, upsellingactivity can be performed by the artificially intelligent orderprocessing system to generate additional sales to maximize the revenuestream. In addition, the present inventor has noted that in aconfiguration as described herein, customer satisfaction is increased ifthe artificially intelligent order processing system is configured toidentify typical preferences of a customer. For example, if the customeris ordering a hamburger, the customer might have a preference for howthe burger is cooked (e.g. medium or well-done; mustard or no mustard).In addition, if the artificially intelligent order processing system isable to identify the customer, the artificially intelligent orderprocessing system optionally queries the customer based upon pastpreferences (e.g. hold the onions on the burger or no ice in the drink).It is a further teaching of an embodiment that alert instances orconditions are defined in the artificially intelligent order processingsystem such that when the alert condition arises an alert to the auditorprompts the auditor to take control of the order processing system or toperform some other corrective action. Lastly, the method as described inthis paragraph further comprises the steps of (1) reviewing an order byan auditor by comparing the order generated by the natural languageprocessor to the communication in the audio stream and (2) updating theorder based upon auditor review.

In another alternative embodiment, the invention comprises a secondmethod of speech recognition-based order processing. The methodcomprises the steps of (1) providing an audio stream of a customer orderto a speech-to text processor; (2) providing an order processor having aspeech recognition module trained using artificial intelligenceprograms; (3) converting a word or words in the audio stream to textusing the speech recognition module; (4) processing the textcommunication with the speech recognition module to identify a word orwords in the text of the converted audio stream according to a previousspoken word training; (5) providing a natural language processor havingorder assembly capabilities and exception detection capabilities,wherein the natural language processor receives recognized text from theorder processor and creates or modifies the order based upon therecognized text; (6) generating an order with the natural languageprocessor; (7) providing an audio stream of a customer order to anauditor; (8) providing the generated order to the auditor; (9)performing a comparison of the audio stream of a customer order with thegenerated order by the auditor; (10) updating the order processor basedupon errors detected in the order by the auditor; and (11) transmittingthe order to a business processor, wherein the business processorcommunicates with a point-of-sale system to collect payment and providenotifications to release the ordered product or service. In anadvantageous embodiment of the artificially intelligent order processingsystem the business processor is configured to receive order informationfrom the NLP and perform the steps of (1) associating the order itemswith one or more additional order items or options associated with theorder items and (2) querying the customer about the one or moreadditional order items or options associated with the order items. TheNLP optionally then updates the order based upon the customer responseto the query. In an embodiment, a text-to-speech processor is configuredto convert queries from the business processor into audio to becommunicated to a customer and process the text with the text-to-speechprocessor to create an audio communication of the query generated by thebusiness processor. In this manner, queries generated by the businessprocessor are utilized to communicate with the customer in associationwith context-relevant communications mechanisms well understood by thoseskilled in the art.

BRIEF DESCRIPTION OF THE DRAWINGS

For a fuller understanding of the invention, reference should be made tothe following detailed description, taken in connection with theaccompanying drawings, in which:

FIG. 1 is a diagram depicting an order system under the control of anon-site employee.

FIG. 2 is a diagram depicting the initial training of an artificiallyintelligent order processing system implemented in a drive throughrestaurant where an on-site employee processes orders and a cloud-basedcrowd-sourcing platform transcribes the audio into text and add metadatatags. The transcribed text then populates an artificial intelligencecomponent and the resulting output is stored for future access.

FIG. 3 is a diagram depicting an artificially intelligent orderprocessing system configured to process orders under auditor review.

FIG. 4 is a diagram depicting an artificially intelligent orderprocessing system configured to process orders and to operateautonomously with an auditor available for error or exception handlingand/or upon the detection of customer frustration or dissatisfaction.

FIG. 5 is a flowchart depicting the training of an artificiallyintelligent order processing system.

FIG. 6 is a diagram depicting an artificially intelligent orderprocessing system configured to process orders while operatingautonomously with an auditor available for error or exception handlingand/or upon the detection of customer frustration or dissatisfaction.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

The preferred embodiment of the invention provides an improvedspeech-based, natural language point-of-sale customer order stationwhich is useful for any business that interacts with customers throughspeech or sound. To aid in the understanding of the preferredembodiment, it will be described in this patent application in thecontext of a drive-through restaurant. The preferred embodimentfacilitates the automated operation of the ordering function of adrive-through restaurant, fast food restaurant or other businessestablishment by replacing an employee or other means of capturing orderdata with an ordering system employing a highly accurate speechrecognition component that is able to be trained to recognize a widevocabulary of words, and associate tones and other metadata in a mannernot previously achieved in speech-to-text systems.

In the exemplary embodiments presented herein, the artificiallyintelligent order processing system is configured to facilitate customerinteractions. A customer interacts with a system according toembodiments through a microphone at an order station where themicrophone transmits the audio signal to be processed for orderprocessing. However, it is envisioned that that the artificiallyintelligent order processing system is otherwise configured tofacilitate customer communication through variety of electronic audiocommunication means, including via telephone.

Embodiments of the invention comprise a order station. In an embodiment,the order station comprises an order panel with a microphone componentand a speaker component. The artificially intelligent order processingsystem in an embodiment is configured to be either voice or speechactivated and optionally incorporates a video display, a touch screenand one or more components to facilitate payment in association withmechanisms well-understood by those skilled in the art. In anembodiment, the video display is used to display menu items (or otheritems for purchase or selection), to display a running list of itemsordered, prices and totals, and order status (e.g., wait time,availability of items).

In an embodiment, the order station further comprises a menu display.The menu display is configured to communicate either via video asdiscussed above, or in fixed form, which, based upon current technology,is preferably a menu display screen, for displaying, on one or morescreens, a complete listing of all available menu items and options withtheir respective prices and other menu item information as desired. If atouch screen and payment components are used they will preferably bepositioned to be reached through the driver's window by a person seatedin the driver's seat of the customer vehicle. Alternatively, if theorder station is a walk-up kiosk, such as inside a restaurant, the touchscreen and payment components will preferably be positioned to bereached by a customer in a standing position. Alternatively, a customersmart phone could be used as the interface in lieu of the order station.

The artificially intelligent natural language order processing system inalternative embodiments is configured to incorporate multiple modes. Invarious embodiments, the order system is configured to operate either inone mode or a plurality of modes depending upon the installation anddesired application. In the preferred embodiment, the intended modes canbroadly be categorized as (i) a training mode, (ii) an auditor-assistmode and (iii) an autonomous mode.

In various embodiments, the artificially intelligent order processingsystem comprises an auditor. In an exemplary configuration, the auditoris employed to ensure the proper functioning of the artificiallyintelligent order processing system after training. In an embodiment,the auditor is physically located off-site from the remainder of theartificially intelligent order processing system. The auditor isoptionally configured or otherwise directed to review the order todetect errors or faults, such as by comparing the processed order inreal time to the audio stream, and override the other aspects or outputsof the artificially intelligent order processing system as appropriate.More specifically, the auditor in an embodiment is configured to (1)review a summary of the current order; (2) see a text of all requestsmade by the customer; (3) view the most recent request (e.g., “Pleaseadd fries.”), and the associated response by the natural languageprocessor (e.g., add 1 fry to order); and (4) approve an intent, oredit, if it is not correct (e.g., The customer said “add one fry” butthe artificially intelligent order processing system heard “add twofries”). In an embodiment, the auditor is configured to have thecapability to switch between modes or switch the artificiallyintelligent order processing system off to pass control to on-siteemployee(s). An administrator (e.g., a manager, etc.) or auditor mayswitch the order system between the modes based on, for example,staffing levels, time of day, and/or customer traffic, etc.

In an embodiment, the artificially intelligent order processing systemis configured to have the capability to operate in a training mode. Inthe training mode (a) an on-site or call center employee listens to thecustomer's speech via a microphonic transmission means and keys orderitems into a POS system, while (b) the audio stream is pushed to atranscription unit where the audio is converted to text and metadatatags are added to the text to add context to the converted text. In anadvantageous embodiment the transcription unit is a crowd-sourcedplatform. As referred to herein, crowdsourcing is a process throughwhich a task, problem or project is solved and completed through aparticipants that may be geographically dispersed. The artificiallyintelligent order processing system is configured to empowerparticipants to listen to audio clips, segments or slices of the audiofeed deriving from other aspects of the artificially intelligent orderprocessing system, convert the audio to text and add metadata tags toenhance the context of the converted text, in an embodiment byleveraging communication and network technologies as well understood bythose skilled in the art. It is envisioned that one person orparticipant could listen to a given clip, or a plurality of participantscould listen and transcribe the same clip depending upon the desiredfidelity sought. For example, three persons (or 5, 7, 9, or more) couldtranscribe the text and add metatags, with the consensus transcriptionpassed to the next step or stage. The audio and converted text with tagsis then used to (a) train an algorithm that can correctly transcribeaudio files into text and (b) train an algorithm that can pull meaningfrom transcribed text (ex. Add one fry to order), both taking advantageof artificial intelligence applications (e.g., a deep neural network) toenhance the fidelity of conversion to text and pulling meaning from thecustomer requests.

In the auditor-assist mode the on-site employee is removed from thetraditional order processing functions and serves only as a back-up totake over the artificially intelligent order processing system upon theincidence of an error or when the artificially intelligent orderprocessing system is switched off by an administrator or auditor. Theaudio stream (also variously referred to as the “audio feed”) is passedfrom the on-site computer to both (1) a speech-to-text processor and (2)to the auditor. One part of the audio stream is transmitted to aspeech-to-text converter where the audio converted to text using aspeech-to-text processor (e.g., Dragon Dictation) for later processinginto an order by a natural language processor. Once the order isprocessed by the NLP an order summary, with instruction sets, is passedto the auditor for review. The other branch of the audio stream istransmitted, as audio, to the auditor. In early stages of systemimplementation, one auditor could service text from one audio stream orone business site. The auditor could compare the audio stream, either inreal-time or with a slight delay, to the transcript of the order asgenerated by a natural language processor. In addition, rather thanauditing each order, the auditor could sample the orders, thereby onlylooking at a subset of orders or orders meeting certain parameters.Furthermore, the auditor could audit the orders only when a triggercondition presents or the auditor could simply function to safeguard theartificially intelligent order processing system and pass control of theartificially intelligent order processing system back to the on-siteemployee when an error situation arises. As the artificially intelligentorder processing system fidelity increases, the auditor is spread overmultiple business locations or customer ordering portals, therebyfacilitating a decrease in the number of auditors. An embodiment of theinvention is configured such that a single auditor could service asingle customer or customer portal, multiple customer portals at asingle business site, three business sites, five business sites, tenbusiness sites, or twenty or more business sites depending upon thefidelity of the artificially intelligent order processing system and therelative traffic level of the business site.

As used herein, the term “processor” is not limited merely to thoseintegrated circuits referred to in the art as a processor, but broadlyrefers to a microcontroller, a microcomputer, a programmable logiccontroller, an application-specific integrated circuit, and any otherprogrammable circuit.

In various embodiments, functionality for implementing the techniques ofthe present invention can be distributed among any number of clientand/or server components. For example, in embodiments, various modulesare implemented for performing various functions in connection with thepresent invention, and such modules can be variously implemented to runon server and/or client components.

According to various embodiments, the order processing system mayinclude a plurality of different types of components, devices, modules,processes, systems, and the like, which, for example, may be implementedand/or instantiated via the use of hardware and/or combinations ofhardware and software.

In the auditor-assist mode the transcription of the audio by the speechtranscription unit is eliminated and the artificially intelligent orderprocessing system operates with auditor overview of the order processingsystem. Errors in an order detected by the auditor can be fixed and fedback into the speech-to-text and natural language processor system toincrease system fidelity and ensure that errors do not propagate ormultiply through the artificially intelligent order processing system.It is further contemplated that the auditor can take-over the orderprocessing system and switch off or override the order processing bycommunicating electronically with the customer and inputting order itemsand other key data into the POS system in the event of an error detectedin the artificially intelligent order processing system or at thedetection of customer dissatisfaction with the order processing system.The present inventor has noted that this feature is particularly usefulwhen there may not be sufficient on-site staff at a restaurant deployingthe artificially intelligent order processing system.

In the autonomous mode the artificially intelligent order processingsystem functions automatically without oversight by an auditor. Theartificially intelligent order processing system can employ errordetection systems to detect deviations in orders or customerdissatisfaction. Such conditions can result in the artificiallyintelligent order processing system switching back to an auditor-assistmode or control of the order processing function passing back to theon-site employee.

Turning to the figures, FIG. 1 is an illustration of a scenario at adrive-through restaurant 12 utilizing an order processing system wherean on-site employee takes orders, receives payment and delivers food tocustomers. Such a system can be employed as a back-up where humanintervention is required when the order-processing system according toaspects of the invention experiences a fault and needs to pass controlof the artificially intelligent order processing system to an on-siteemployee.

In an embodiment of the invention, the artificially intelligent orderprocessing system is configured such that the arrival of a customer 25at an order station triggers an alert to an employee 81 or the customer25 may make their presence known by speaking into a microphone 21. Atransaction is initiated when a speaker integrated into the artificiallyintelligent order processing system 22 plays an audio greeting to acustomer 25 to alert the customer 25 that he has been recognized by theartificially intelligent order processing system as having arrived atthe ordering station. The audio greeting can be as simple as on-siteemployee 81 saying “Hello. May I take your order?” Alternatively, apre-recorded message can be played by speaker 22 to let the customer 25know that the on-site employee 81 will be with the customer shortly.Customer 25 then speaks into microphone 21 to request menu items or askquestions. The audio is passed from speaker 21 to a base station 80within the restaurant. The base station 80 then wirelessly transmits theaudio to a headset worn by employee 81. The headset worn by the employeewill typically have one or two earphones to transmit the audio inputfrom the customer 25 to the on-site employee 81 and a microphone toenable the on-site employee 81 to communicate in return with thecustomer 25. Speech emanating from the onsite employee 81 will enter themicrophone on the headset to be transmitted to the customer using thespeaker 22 at the order station. The on-site employee 81 will thenmanually key in the order into the on-site point of sale (POS) system61. The on-site POS system 61 then transmits the order to a terminal 23having a screen at the order station for viewing and confirmation by thecustomer 25. Alternatively, the onsite employee 81 reads the order backto the customer 25 for confirmation. The customer 25 then pays for theorder using terminal 23 (e.g. by swiping a credit card) or the customer25 submits payment directly to on-site employee 81. Alternatively, ifpayment is made directly from the customer 25 to on-site employee 81,then the on-site employee 81 must key payment into the on-site POSsystem 61. Payment made through terminal 23 will pass automatically tothe on-site POS system 61. The on-site POS system 61 then associatespayment with an order and generates a status indicator that payment hasbeen received so that on-site employee 81 may fulfill the order bydelivering the ordered food to the customer 25. Food delivery to thecustomer will typically occur at a food delivery station, and not theorder station, to facilitate the flow of customers through theartificially intelligent order processing system. In an embodiment, theartificially intelligent order processing system is configured such thatthe on-site POS system 61 is linked to a POS Relay 62 to keep track ofrevenue, inventory and the like.

FIG. 2 is a diagram depicting the components and the flow of data in anorder processing system 10 according to aspects of an embodiment of theinvention. In the artificially intelligent order processing systemaccording to FIG. 2 the artificially intelligent order processing systemis in training mode and the on-site employee 81 is responsible fortaking orders, taking payment, and delivering food to a customer 25.However, in an embodiment, the artificially intelligent order processingsystem is configured the audio feed from order station microphone 21 totrain the artificially intelligent order processing system 10 for futureimplementation and substitution of the on-site employee 81 in the orderfunction. Accordingly, an on-site computer 31 is added to theartificially intelligent order processing system in an embodiment asdepicted in FIG. 1. The on-site computer 31 is placed in the audiostream after the base station 80 to enable the artificially intelligentorder processing system 10 to “listen” to audio communications occurringbetween a customer and an employee in that specific business (e.g.McDonalds™, Chik-Fil-A™ or Bank of America) or business segment (e.g.drive-through restaurant, drive-up bank, pick-up window at a drug store,or payment gate for a parking lot), thus facilitating the training of adeep neural network (DNN) system (e.g. DNN).

The on-site computer 31 will have an audio input component for receivingthe audio generated from an order station microphone 21. The amplifiedsignal from the order station microphone 21 is typically about 10 W.This amplified signal needs to be stepped down for the artificiallyintelligent order processing system 10 to listen to the audiocommunication from the customer through the microphone 21 to the basestation 80. The impedance of the two systems needs to be matched so theaudio can be properly passed to the onsite computer 31, so an attenuatoris used to normalize the two systems. The attenuator is capable of beingadjusted to address the scenario where the output from the restaurantcommunication system is higher or lower than expected. This facilitatesthe adjustment of the incoming volume to the on-site computer 31. Theonsite computer 31 is also capable of adjusting the gain to increase thevolume for the input. An USB to GPIO interface, configured usingsoftware, is utilized for both input and output to the on-site computer31. In further implementations of the artificially intelligent orderprocessing system, such as where audio is transmitted from the cloud,back through the on-site computer 31 and to speaker 22, the audio signalwill require amplification as it leaves the on-site computer 31.

In an embodiment, the audio stream starting at order station microphone21 is transmitted to the base station 80. If the computer 31 is switched“off” then the audio stream will be transmitted only to the on-siteemployee 81. If the on-site computer 31 is switched “on” then the audiostream will both be (1) transmitted to the on-site employee 81 and (2)pushed up to the cloud. The artificially intelligent order processingsystem is optionally configured to be switched on or off remotely or atthe on-site location depending upon the needs of the business and theartificially intelligent order processing system performing thetraining.

Following transmission to the cloud, a first step in processing theaudio feed can be slicing the feed into component transcribed text oraudio clips 43. If a sliced clip meets certain parameters, the audioclip can be passed to the transcription unit 44, such as a crowd sourceplatform. If the audio clip does not meet specific parameters, then theaudio clip can be discarded. So, for instance, if the artificiallyintelligent order processing system is engaged in training to serve afast food restaurant specializing in hamburgers, the artificiallyintelligent order processing system is optionally configured to producean audio stream stating: “Wow! There are so many items to choose from.But it looks like breakfast is over? [pause] I want a cheeseburger.” Theartificially intelligent order processing system optionally parse theaudio stream into three components or audio clips, such as, for example:(1) “Wow! There are so many items to choose from.”; (2) “But it lookslike breakfast is over?”; and (3) “I want a cheeseburger.”. The firsttwo audio clips optionally are provided outside the set parameters anddiscarded, while the third audio clip of “I want a cheeseburger.” meetsthe parameters and is passed for transcription by a transcription unit44 for conversion from speech to text, such as by the crowd sourceplatform. In an embodiment of the invention, by setting parameters anddiscarding clips outside of the parameters, the artificially intelligentorder processing system is configured to prevent the transcription unit44 from transcribing superfluous audio.

The selected audio clip is passed to the transcription unit 44 where theaudio is converted to text. The transcription unit can be a crowd sourceplatform where individuals listen to the audio clip and manually convertthe audio to text in the form of a “transcribed clip.” In addition,metadata tags are optionally added to the transcribed clip text toprovide additional context for the audio clip. For example, frustrationlevels in the customers voice, the approximate age of the customer, theapproximate gender of the customer and the approximate demographics ofthe customer. A metatag is optionally associated with the transcribedtext and audio clip, optionally by participants in the crowd sourceplatform, when transcribed that would include this metadata gleaned fromthe audio clip. In this manner, the artificially intelligent orderprocessing system us configured to utilize the crowd source platform tosanitize and validate the data. In a specific configuration, theartificially intelligent order processing system utilizes humans totranscribe the audio clips in the initial or training phase to increasethe fidelity of the artificially intelligent order processing systemover that achieved when the artificially intelligent order processingsystem is configured to utilize machine-based speech-to-text processors.

The audio, text, and metadata from the audio clips are then transmittedfrom the transcription unit 44 to a Deep Neural Network (DNN), wherethey are used to develop a Speech-to-Text System 41. Such transmissionis intended to provide the ability to approximately detect frustrationlevels, age, gender and demographics of future customers. The DNNreceives the audio, text and metadata and runs specific trainingprograms on each of these items. The artificially intelligent orderprocessing system in an embodiment is configured to utilize the audio tobuild a pattern recognition program constituting a speech-to-textmodule. Thus, when the artificially intelligent order processing systemor speech-to-text module element encounters an audio clip in a liveenvironment, the artificially intelligent order processing system orspeech-to-text module element can quickly relate the audio clip to otheraudio clips encountered during training, and then cross-reference theaccompanying text with those audio clips. In this manner, thespeech-to-text converter infers the text from the audio based oncomparison of the new live audio with the past training audio.

A Speech-to-Text System 41 will be able to take subsequent clips oraudio streams from a customer and associate them with prior patterns todetermine whether the utterance resulting in the audio conforms to a setof words, instructions, questions, etc. thereby identifying the spokenwords that were transmitted.

In embodiments of the invention, artificial intelligence, such as thatdeveloped within the DNN in accordance with the mechanisms describedherein, is leveraged to enhance the recognition of words or phrases. Ina similar fashion, machine learning is intimately connected into theartificial intelligence. In embodiments of the invention, as theartificial intelligence begins to build a database of information aboutcustomers, their preferences, common questions, and requests, theartificially intelligent order processing system builds upon itsexisting knowledge base in order to provide new and unique ways ofresponding to future users who have similar requests and needs. Usersare also able to enhance the internal machine learning capabilities ofthe artificially intelligent order processing system through voice andtext inputs to expand the artificially intelligent order processingsystems knowledge base in accordance with teachings of the invention.

Embodiments of the invention incorporate speech-based trainingalgorithms. Such speech-based training algorithms involve the process ofusing spoken language to provide feedback to an artificially intelligentsoftware application. This feedback in a embodiments is used in aconfiguration of the artificially intelligent order processing system tomodify the responses the artificial intelligence program would giveresponsive to a customer request or order item.

An embodiment of the artificially intelligent order processing system isconfigured such that the transcribed text from the audio clips are thentransmitted from the Speech-to-Text System 41 to a Natural LanguageProcessor 51, also employing a Deep Neural Network, where meaning ispulled from, or assigned to, the speech following its recognition by theSpeech-to-Text System 41. In the initial training stage the audio, text,and metadata from the clips are transmitted from the Natural LanguageProcessor 51 to a storage device 55 for future access. In an embodiment,storing data facilitates tasks including re-testing the artificiallyintelligent order processing system, such as in instances where theartificially intelligent order processing system encounters difficultyin the identification of certain, words, phrases or tones.

Referring back to the order station, in certain embodiments the orderpanel comprises an automated, self-service payment acceptor. The paymentacceptor optionally comprises a magnetic strip card reader or chipreader (e.g., employing the EMV standard) for credit cards, debit cards,EBT cards and other types of widely used cards which utilize a magneticstrip or chip. In an embodiment, the payment acceptor further comprisesmobile payment options like Android or Apple Pay. Additionally, thepayment acceptor is optionally configured to utilize distinguishingfactors of a user, such as distinguishing factors derived from facialrecognition, to connect a user with a stored payment account forautomatically making payment after the order has been placed.

In an embodiment, the artificially intelligent order processing systemis configured to communicatively connect with a restaurant such that ifthe customer 25 has a mobile payment account setup with the restaurant,the receipt for the order is sent to the customer's device as a recordof their transaction.

The training system in an embodiment is configured to capture thelanguage necessary to operate the artificially intelligent orderprocessing system and associate the language with menu items andrequests for service as required to apply in a drive-up restaurantenvironment. In the training phase the artificially intelligent orderprocessing system will generally not be operable to process orders, butwill piggy-back on the work of employees performing restaurant functionsincluding audibly capturing order instructions, converting those audibleinstructions to input data for the point of sale system, and confirmingthat an order has been captured correctly for further preparation andpayment collection.

FIG. 3 is a diagram depicting the components and the flow of data in anorder processing system 10 according to additional aspects of theinvention. In the artificially intelligent order processing systemaccording to FIG. 3 an auditor 71 is added to the artificiallyintelligent order processing system to ensure the fidelity of the orderprocess and recognition of speech by the artificially intelligent orderprocessing system 10. The artificially intelligent order processingsystem depicted in FIG. 3 also shows the artificially intelligent orderprocessing system 10 taking over the order processing function from theon-site employee 81.

As in the training example in FIG. 2, in the artificially intelligentorder processing system depicted in FIG. 3 the audio stream that startsat the order station microphone 21 is transmitted to the base station 80and then the on-site computer 31. If the computer 31 is switched “off”then the audio stream will be transmitted only to the on-site restaurantbase station. If the computer 31 is switched “on” then the audio streamwill both be (1) transmitted to the on-site restaurant base station and(2) transmitted to the cloud 14. It is contemplated that theartificially intelligent order processing system 10 can be switched onor off remotely or at the on-site location depending upon the needs ofthe business and the artificially intelligent order processing systemperforming the training. The on-site employee 81 in an embodiment isstill be responsible for taking payment, and delivering food to acustomer 25, although these functions are not depicted in FIG. 3.

Following transmission to the cloud 14, in a configuration of theartificially intelligent order processing system, the first step inprocessing the audio stream comprises running the audio stream throughthe trained speech-to-text module 41 created in the training phase, suchas in the process presented in FIG. 2. The audio stream from the on-sitecomputer 31 is also optionally transmitted to an auditor 71, who is ableto listen to the audio of the order. The speech-to-text converter 41outputs the transcribed text in its entirety, to a natural languageprocessor (NLP) 51.

In an embodiment, artificially intelligent order processing systemfurther comprises a natural language processor (NLP) 51. The NLP 51pulls meaning out of the text. In an example, when the text comprises “Iwant a cheeseburger.”, or some similar statement, is sent to the NLP 51,an instruction set adding one cheeseburger to the order is generated,representing the intent of the order. This order, with an order summaryin text form (e.g., the order intent—ex. “add one cheeseburger”), ispassed to the auditor 71 for comparison with the audio feed that waspassed to the auditor 71 from the on-site computer 31. If the ordergenerated by the NLP 51 is correct, then the auditor 71 approves thesubmitted intent from the NLP 51, then the artificially intelligentorder processing system passes the instructions to the businessprocessor 52 and the audio and accompanying order intent is stored in astorage medium 55. If the auditor 71 detects that the artificiallyintelligent order processing system 10 is not functioning properly, orthat the customer 25 is frustrated or otherwise not being adequatelyserved by the artificially intelligent order processing system 10, thenthe auditor 71 can abort, or intervene in, the transaction and passcontrol back to the on-site employee 81, such as by sending anotification to the employee 81, including a possible description of theproblem or error. The auditor 71 can also remotely switch off theon-site computer 31, to allow on-site employees 81 to take control oforder processing.

An embodiment of the artificially intelligent order processing systemcomprises a business processor 52. The business processor 52 is wherebusiness logic operations are performed. For example, if the customer 25ordered a cheeseburger, the instruction set for the business processorwould be to output the text “would you like fries and a drink withthat?” The text would be sent to text-to-speech module 42 and then theon-site computer 31, which will output the audio to the base station 80and then out to the speaker 22. Simultaneously, the business processorwill send the original intent of ‘add one cheeseburger’ to the POS Relay62, which would send the order down to the onsite point of sale system61.

An embodiment of the artificially intelligent order processing system isconfigured to promote sales. An additional feature of the proposedsystems is its ability to effectively promote sales in a manneranalogous a human cashier. Customers interacting with fast foodemployees are well-accustomed to the standard up-selling phrase “wouldyou like to make it a combo?” The present inventor has recognized thatthis particular phrase and variants are used so commonly because it iseffective in increasing sales, and thereby is a teaching of anembodiment of the system. The present inventor has further recognizedthat any new system must be able to provide such promotional features aseffectively as a human cashier, and thereby is a teaching of anembodiment of the system. According to an embodiment, the artificiallyintelligent order processing system is configured to offer additionalitems that are frequently purchased together or based upon promotions atthe business. For example, if the customer orders an item that is alsosold as part of a “combo” with additional menu options, the artificiallyintelligent order processing system could prompt the customer and informthe customer what combos are available for purchase. In addition, by wayof example, if bacon or cheese is frequently added to an order, theartificially intelligent order processing system could prompt thecustomer by asking them if they would like to add that item to theirorder. In a similar manner, and in conjunction with technology currentlyavailable to recognize the identity of a customer and track theirpreferences with regard to past orders, the artificially intelligentorder processing system could query the customer to determine if thosepast selections should be added to their current order.

FIG. 4 is a diagram depicting the components and the flow of data in anorder processing system 10 according to additional aspects of anembodiment. In the artificially intelligent order processing systemaccording to FIG. 4 the artificially intelligent order processing system10 is largely operating autonomously and the auditor is presentpredominately to provide exception handling when the artificiallyintelligent order processing system is not functioning properly. In theautonomous mode the artificially intelligent order processing systemfunctions automatically without direct and continuous oversight by anauditor. The artificially intelligent order processing system can employautomatic error detection systems to detect deviations in orders orcustomer dissatisfaction. Such conditions can result in the artificiallyintelligent order processing system switching back to an auditor-assistmode or control of the order processing function passing back to theon-site employee.

A point-of-sale commercial transaction processing system 62 based oncloud computing and intelligent analysis, particularly suitable for thefast food industry, constitutes a teaching of an embodiment of theinvention. The transaction processing system utilizes an order terminalhaving an audio speaker, a microphone, and preferably a video display, acomputer system in communication with the order terminal and runningartificial intelligence routines to process or pre-process verbalrequests provided into the microphone of the order terminal, and ahuman-controlled response system, or auditor, which completes, correctsor verifies requests that cannot be satisfactorily completed by theartificial intelligence routines alone. The auditor is preferably incommunication with the order terminal or (and the customer) via ahigh-speed voice over internet protocol (VoIP) or data connection. Theorder terminal may further include payment terminals, touch screens, andother hardware and software options to facilitate the receipt of paymentby the customer.

Various techniques have been described in detail above with reference toa few example embodiments thereof as illustrated in the accompanyingdrawings. In the preceding description, numerous specific details areset forth in order to provide a thorough understanding of one or moreaspects and/or features described or reference herein. It will beapparent, however, to one skilled in the art, that one or more aspectsand/or features described or referenced herein may be practiced withoutsome or all of these specific details. In other instances, well knownprocess steps and/or structures have not been described in detail inorder to not obscure some of the aspects and/or features described orreference herein.

One or more different inventions may be described in the presentapplication.

Further, for one or more of the invention(s) described herein, numerousembodiments may be described in this patent application, and arepresented fix illustrative purposes only. The described embodiments arenot intended to be limiting in any sense. One or more of theinvention(s) may be widely applicable to numerous embodiments, as isreadily apparent from the disclosure. These embodiments are described insufficient detail to enable those skilled in the art to practice one ormore of the invention(s), and it is to be understood that otherembodiments may be utilized and that structural, logical, software,electrical and other changes may be made without departing from thescope of the one or more of the invention(s). Accordingly, those skilledin the art will recognize that the one or more of the invention(s) maybe practiced with various modifications and alterations. Particularfeatures of one or more of the invention(s) may be described withreference to one or more particular embodiments or figures that form apart of the present disclosure, and in which are shown, by way ofillustration, specific embodiments of one or more of the invention(s).It should be understood, however, that such features are not limited tousage in the one or more particular embodiments or figures withreference to Which they are described. The present disclosure is neithera literal description of all embodiments of one or more of theinvention(s) nor a listing of features of one or more of theinvention(s) that must be present in all embodiments.

Headings of sections provided in this patent application and the titleof this patent application are for convenience only, and are not to betaken as limiting the disclosure in any way.

Devices that are in communication with each other need not be incontinuous communication with each other, unless expressly specifiedotherwise. In addition, devices that are in communication with eachother may communicate directly or indirectly through one or moreintermediaries.

A description of an embodiment with several components in communicationwith each other does not imply that all such components are required. Tothe contrary, a variety of optional components are described toillustrate the wide variety of possible embodiments of one or more ofthe invention(s).

Further, although process steps, method steps, algorithms or the likemay be described in a sequential order, such processes, methods andalgorithms may be configured to work in alternate orders. In otherwords, any sequence or order of steps that may be described in thispatent application does not, in and of itself, indicate a requirementthat the steps be performed in that order. The steps of describedprocesses may be performed in any order practical. Further, some stepsmay be performed simultaneously despite being described or implied asoccurring non-simultaneously (e.g., because one step is described afterthe other step). Moreover, the illustration of a process by itsdepiction in a drawing does not imply that the illustrated process isexclusive of other variations and modifications thereto, does not implythat the illustrated process or any of its steps are necessary to one ormore of the invention(s), and does not imply that the illustratedprocess is preferred.

When a single device or article is described, it will be readilyapparent that more than one device/article (whether or not theycooperate) may be used in place of a single device/article. Similarly,where more than one device or article is described (whether or not theycooperate), it will be readily apparent that a single device/article maybe used in place of the more than one device or article.

The functionality and/or the features of a device may be alternativelyembodied by one or more other devices that are not explicitly describedas having such functionality/features. Thus, other embodiments of one ormore of the invention(s) need not include the device itself.

Techniques and mechanisms described or reference herein will sometimesbe described in singular form for clarity. However, it should be notedthat particular embodiments include multiple iterations of a techniqueor multiple instantiations of a mechanism unless noted otherwise.

The present invention has been described in particular detail withrespect to possible embodiments. Those of skill in the art willappreciate that the invention may be practiced in other embodiments.First, the particular naming of the components, capitalization of terms,the attributes, data structures, or any other programming or structuralaspect is not mandatory or significant, and the mechanisms thatimplement the invention or its features may have different names,formats, or protocols. Further, the artificially intelligent orderprocessing system may be implemented via a combination of hardware andsoftware, as described, or entirely in hardware elements, or entirely insoftware elements. Also, the particular division of functionalitybetween the various system components described herein is merelyexemplary, and not mandatory; functions performed by a single systemcomponent may instead be performed by multiple components, and functionsperformed by multiple components may instead be performed by a singlecomponent.

In various embodiments, the present invention can be implemented as asystem or a method for performing the above-described techniques, eithersingly or in any combination. In another embodiment, the presentinvention can be implemented as a computer program product comprising anontransitory computer-readable storage medium and computer programcode, encoded on the medium, for causing a processor in a computingdevice or other electronic device to perform the above-describedtechniques.

Reference in the specification to “one embodiment” or to “an embodiment”means that a particular feature, structure, or characteristic describedin connection with the embodiments is included in at least oneembodiment of the invention. The appearances of the phrase “in oneembodiment” in various places in the specification are not necessarilyall referring to the same embodiment.

Some portions of the above are presented in terms of algorithms andsymbolic representations of operations on data bits within a memory of acomputing device. These algorithmic descriptions and representations arethe means used by those skilled in the data processing arts to mosteffectively convey the substance of their work to others skilled in theart. An algorithm is here, and generally, conceived to be aself-consistent sequence of steps (instructions) leading to a desiredresult. The steps are those requiring physical manipulations of physicalquantities. Usually, though not necessarily, these quantities take theform of electrical, magnetic or optical signals capable of being stored,transferred, combined, compared and otherwise manipulated. It isconvenient at times, principally for reasons of common usage, to referto these signals as bits, values, elements, symbols, characters, terms,numbers, or the like. Furthermore, it is also convenient at times, torefer to certain arrangements of steps requiring physical manipulationsof physical quantities as modules or code devices, without loss ofgenerality.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise as apparent from the following discussion,it is appreciated that throughout the description, discussions utilizingterms such as “processing” or “computing” or “calculating” or“displaying” or “determining” or the like, refer to the action andprocesses of a computer system, or similar electronic computing moduleand/or device, that manipulates and transforms data represented asphysical (electronic) quantities within the computer system memories orregisters or other such information storage, transmission or displaydevices.

Certain aspects of the present invention include process steps andinstructions described herein in the form of an algorithm. It should benoted that the process steps and instructions of the present inventioncan be embodied in software, firmware and/or hardware, and when embodiedin software, can be downloaded to reside on and be operated fromdifferent platforms used by a variety of operating systems.

The present invention also relates to an apparatus for performing theoperations herein. This apparatus may be specially constructed for therequired purposes, or it may comprise a general-purpose computing deviceselectively activated or reconfigured by a computer program stored inthe computing device. Such a computer program may be stored in acomputer readable storage medium, such as, but is not limited to, anytype of disk including floppy disks, optical disks, CD-ROMs,magnetic-optical disks, read-only memories (ROMs), random accessmemories (RAMs), EPROMs, EEPROMs, magnetic or optical cards, applicationspecific integrated circuits (ASICs), or any type of media suitable forstoring electronic instructions, and each coupled to a computer systembus. Further, the computing devices referred to herein may include asingle processor or may be architectures employing multiple processordesigns for increased computing capability.

The algorithms and displays presented herein are not inherently relatedto any particular computing device, virtualized system, or otherapparatus. Various general-purpose systems may also be used withprograms in accordance with the teachings herein, or it may proveconvenient to construct more specialized apparatus to perform therequired method steps. The required structure for a variety of thesesystems will be apparent from the description provided herein. Inaddition, the present invention is not described with reference to anyparticular programming language. It will be appreciated that a varietyof programming languages may be used to implement the teachings of thepresent invention as described herein, and any references above tospecific languages are provided for disclosure of enablement and bestmode of the present invention.

While the invention has been described with respect to a limited numberof embodiments, those skilled in the art, having benefit of the abovedescription, will appreciate that other embodiments may be devised whichdo not depart from the scope of the present invention as describedherein. In addition, it should be noted that the language used in thespecification has been principally selected for readability andinstructional purposes, and may not have been selected to delineate orcircumscribe the inventive subject matter. Accordingly, the disclosureof the present invention is intended to be illustrative, but notlimiting, of the scope of the invention, which is set forth in theclaims.

All references cited in the present application are incorporated intheir entirety herein by reference to the extent not inconsistentherewith.

It will be seen that the advantages set forth above, and those madeapparent from the foregoing description, are efficiently attained andsince certain changes may be made in the above construction withoutdeparting from the scope of the invention, it is intended that allmatters contained in the foregoing description or shown in theaccompanying drawings shall be interpreted as illustrative and not in alimiting sense.

It is also to be understood that the following claims are intended tocover all of the generic and specific features of the invention hereindescribed, and all statements of the scope of the invention which, as amatter of language, might be said to fall therebetween. Now that theinvention has been described,

What is claimed is:
 1. A method comprising the steps of: providing anaudio stream of an order of a customer positioned on a site; providingan order processor having a speech recognition module trained usingartificial intelligence programs; converting a word or words in theaudio stream to text using the speech recognition module; processing thetext communication with the speech recognition module to identify a wordor words in the text of the converted audio stream according to aprevious spoken word training; providing a natural language processorhaving order assembly capabilities and exception detection capabilities,wherein the natural language processor receives recognized text from theorder processor and creates or modifies an order based upon therecognized text; generating an order with the natural languageprocessor; alerting an auditor of detected exceptions in the order, theauditor located off-site and connected to the order processor via a dataconnection; deactivating the order processor with the auditor inresponse to the detected exceptions in the order; and prompting anon-site employee to engage the customer and complete the order.
 2. Themethod according to claim 1 wherein the order is processed in real time.3. The method according to claim 1 wherein the alert to the auditorprompts the auditor to switch the order processor from an autonomousmode to an auditor-assist mode prior to deactivating the orderprocessor.
 4. A method comprising the steps of: providing an audiostream of an order of a customer to a speech-to text processor, thecustomer positioned on a site; providing an order processorincorporating a speech recognition module trained using artificialintelligence programs; converting a word or words in the audio stream totext using the speech recognition module; processing the textcommunication with the speech recognition module to identify a word orwords in the text of the converted audio stream according to a previousspoken word training; providing a natural language processor havingorder assembly capabilities and exception detection capabilities,wherein the natural language processor receives recognized text from theorder processor and creates or modifies the order based upon therecognized text; generating an order with the natural languageprocessor; providing an audio stream of a customer order to an auditor,the auditor located off-site and connected to the order processor via adata connection; providing the generated order to the auditor;performing a comparison of the audio stream of a customer order with thegenerated order by the auditor; switching to an auditor-assist mode inresponse to the comparison of the audio stream with the generated order;updating the order processor based upon errors detected in the order bythe auditor; deactivating the order processor with the auditor; andtransmitting the generated order to an on-site employee to communicatewith the customer, to collect payment from the customer, and to providenotifications to release the ordered product or service to the customer.5. The method according to claim 4 wherein a business processor receivesorder information from the natural language processor (NLP) and performsthe steps of: associating the order items with one or more additionalmenu items or options associated with the order items; and querying thecustomer about the one or more additional menu items or optionsassociated with the order items, wherein the NLP updates the order basedupon the customer response to the query.
 6. The method according toclaim 5 further comprising the steps of: providing a text-to-speechprocessor to convert queries from the business processor into audio tobe communicated to a customer; and processing the text with thetext-to-speech processor to create an audio communication of the querygenerated by the business processor.
 7. The method according to claim 4further comprising the steps of reviewing an order by the auditor bycomparing the order generated by the natural language processor to thecommunication in the audio stream and updating the order based uponauditor review.
 8. The method according to claim 4 further comprisingthe steps of: providing a text-to-speech processor to convert queriesfrom the business processor into audio to be communicated to a customer;and processing the text with the text-to-speech processor to create anaudio communication of the query generated by the business processor. 9.The method according to claim 4 further comprising the step of alertingthe auditor by the natural language processor (NLP) of any exceptionsdetected by the artificially intelligent order processing system,wherein the auditor can override the artificially intelligent orderprocessing system based upon the detected exception.
 10. The methodaccording to claim 4 wherein the auditor deactivates the order processorin response to a detected change in customer traffic.
 11. The methodaccording to claim 4 wherein the auditor deactivates the order processorin response to a detected change in staffing at the site.